Create app.py

app.py

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+import streamlit as st
+
+
+#This module provides a way to interact with the operating system, such as accessing environment variables, working with files
+#and directories, executing shell commands, etc
+import os
+
+#Helps us generate embeddings
+#An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness. 
+#Small distances suggest high relatedness and large distances suggest low relatedness.
+from langchain.embeddings import OpenAIEmbeddings
+
+
+#FAISS is an open-source library developed by Facebook AI Research for efficient similarity search and clustering of large-scale datasets, particularly with high-dimensional vectors. 
+#It provides optimized indexing structures and algorithms for tasks like nearest neighbor search and recommendation systems.
+from langchain.vectorstores import FAISS
+
+
+#load_dotenv() is a function that loads variables from a .env file into environment variables in a Python script. 
+#It allows you to store sensitive information or configuration settings separate from your code
+#and access them within your application.
+from dotenv import load_dotenv
+
+
+load_dotenv()
+
+
+#By using st.set_page_config(), you can customize the appearance of your Streamlit application's web page
+st.set_page_config(page_title="Educate Kids", page_icon=":robot:")
+st.header("Hey, Ask me something & I will give out similar things")
+
+#Initialize the OpenAIEmbeddings object
+embeddings = OpenAIEmbeddings()
+
+#The below snippet helps us to import CSV file data for our tasks
+from langchain.document_loaders.csv_loader import CSVLoader
+loader = CSVLoader(file_path='myData.csv', csv_args={
+    'delimiter': ',',
+    'quotechar': '"',
+    'fieldnames': ['Words']
+})
+
+#Assigning the data inside the csv to our variable here
+data = loader.load()
+
+#Display the data
+print(data)
+
+db = FAISS.from_documents(data, embeddings)
+
+#Function to receive input from user and store it in a variable
+def get_text():
+    input_text = st.text_input("You: ", key= input)
+    return input_text
+
+
+user_input=get_text()
+submit = st.button('Find similar Things')  
+
+if submit:
+    
+    #If the button is clicked, the below snippet will fetch us the similar text
+    docs = db.similarity_search(user_input)
+    # print(docs)
+    st.subheader("Top Matches:")
+    st.text(docs[0])
+    st.text(docs[1].page_content)